Source code for torcheeg.datasets.module.emotion_recognition.seed_iv_feature
import os
import re
from typing import Any, Callable, Dict, Tuple, Union
import numpy as np
import scipy.io as scio
from torcheeg.io import EEGSignalIO, MetaInfoIO
from ...constants.emotion_recognition.seed_iv import (
SEED_IV_ADJACENCY_MATRIX, SEED_IV_CHANNEL_LOCATION_DICT)
from ..base_dataset import BaseDataset
[docs]class SEEDIVFeatureDataset(BaseDataset):
r'''
The SEED-IV dataset provided by the BCMI laboratory, which is led by Prof. Bao-Liang Lu. Since the SEED dataset provides features based on matlab, this class implements the processing of these feature files to initialize the dataset. The relevant information of the dataset is as follows:
- Author: Zheng et al.
- Year: 2018
- Download URL: https://ieeexplore.ieee.org/abstract/document/8283814
- Reference: Zheng W L, Liu W, Lu Y, et al. Emotionmeter: A multimodal framework for recognizing human emotions[J]. IEEE transactions on cybernetics, 2018, 49(3): 1110-1122.
- Stimulus: 168 film clips.
- Signals: Electroencephalogram (62 channels at 200Hz) and eye movement data of 15 subjects (8 females). Each subject conducts the experiments in three sessions, and each session contains 24 trials (6 per emotional category) totally 15 people x 3 sessions x 24 trials.
- Rating: neutral (0), sad (1), fear (2), and happy (3).
- Features: de_movingAve, de_LDS, psd_movingAve, psd_LDS, dasm_movingAve, dasm_LDS, rasm_movingAve, rasm_LDS, asm_movingAve, asm_LDS, dcau_movingAve, dcau_LDS of 4-second long windows
In order to use this dataset, the download folder :obj:`eeg_feature_smooth` is required, containing the following folder:
- 1
- 2
- 3
An example dataset for CNN-based methods:
.. code-block:: python
dataset = SEEDIVFeatureDataset(io_path=f'./seed',
root_path='./eeg_feature_smooth',
features=['de_movingAve'],
offline_transform=transforms.ToGrid(SEED_CHANNEL_LOCATION_DICT),
online_transform=transforms.ToTensor(),
label_transform=transforms.Select('emotion'))
print(dataset[0])
# EEG signal (torch.Tensor[5, 9, 9]),
# coresponding baseline signal (torch.Tensor[5, 9, 9]),
# label (int)
An example dataset for GNN-based methods:
.. code-block:: python
dataset = SEEDIVFeatureDataset(io_path=f'./seed',
root_path='./eeg_feature_smooth',
features=['de_movingAve'],
online_transform=ToG(SEED_ADJACENCY_MATRIX),
label_transform=transforms.Select('emotion'))
print(dataset[0])
# EEG signal (torch_geometric.data.Data),
# coresponding baseline signal (torch_geometric.data.Data),
# label (int)
In particular, TorchEEG utilizes the producer-consumer model to allow multi-process data preprocessing. If your data preprocessing is time consuming, consider increasing :obj:`num_worker` for higher speedup. If running under Windows, please use the proper idiom in the main module:
.. code-block:: python
if __name__ == '__main__':
dataset = SEEDIVFeatureDataset(io_path=f'./seed',
root_path='./eeg_feature_smooth',
feature=['de_movingAve'],
offline_transform=transforms.ToGrid(SEED_CHANNEL_LOCATION_DICT),
online_transform=transforms.ToTensor(),
label_transform=transforms.Select('emotion'),
num_worker=4)
print(dataset[0])
# EEG signal (torch_geometric.data.Data),
# coresponding baseline signal (torch_geometric.data.Data),
# label (int)
Args:
root_path (str): Downloaded data files in matlab (unzipped ExtractedFeatures.zip) formats (default: :obj:`'./ExtractedFeatures'`)
feature (list): A list of selected feature names. The selected features corresponding to each electrode will be concatenated together. Feature names supported by the SEED dataset include de_movingAve, de_LDS, psd_movingAve, and etc. (default: :obj:`['de_movingAve']`)
num_channel (int): Number of channels used, of which the first 62 channels are EEG signals. (default: :obj:`62`)
online_transform (Callable, optional): The transformation of the EEG signals and baseline EEG signals. The input is a :obj:`np.ndarray`, and the ouput is used as the first and second value of each element in the dataset. (default: :obj:`None`)
offline_transform (Callable, optional): The usage is the same as :obj:`online_transform`, but executed before generating IO intermediate results. (default: :obj:`None`)
label_transform (Callable, optional): The transformation of the label. The input is an information dictionary, and the ouput is used as the third value of each element in the dataset. (default: :obj:`None`)
before_trial (Callable, optional): The hook performed on the trial to which the sample belongs. It is performed before the offline transformation and thus typically used to implement context-dependent sample transformations, such as moving averages, etc. The input of this hook function is a 3D EEG signal with shape (number of windows, number of electrodes, number of features), whose ideal output shape is also (number of windows, number of electrodes, number of features).
after_trial (Callable, optional): The hook performed on the trial to which the sample belongs. It is performed after the offline transformation and thus typically used to implement context-dependent sample transformations, such as moving averages, etc. The input and output of this hook function should be a sequence of dictionaries representing a sequence of EEG samples. Each dictionary contains two key-value pairs, indexed by :obj:`eeg` (the EEG signal matrix) and :obj:`key` (the index in the database) respectively.
io_path (str): The path to generated unified data IO, cached as an intermediate result. (default: :obj:`./io/seed_iv_feature`)
io_size (int): Maximum size database may grow to; used to size the memory mapping. If database grows larger than ``map_size``, an exception will be raised and the user must close and reopen. (default: :obj:`10485760`)
io_mode (str): Storage mode of EEG signal. When io_mode is set to :obj:`lmdb`, TorchEEG provides an efficient database (LMDB) for storing EEG signals. LMDB may not perform well on limited operating systems, where a file system based EEG signal storage is also provided. When io_mode is set to :obj:`pickle`, pickle-based persistence files are used. (default: :obj:`lmdb`)
num_worker (int): Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: :obj:`0`)
verbose (bool): Whether to display logs during processing, such as progress bars, etc. (default: :obj:`True`)
in_memory (bool): Whether to load the entire dataset into memory. If :obj:`in_memory` is set to True, then the first time an EEG sample is read, the entire dataset is loaded into memory for subsequent retrieval. Otherwise, the dataset is stored on disk to avoid the out-of-memory problem. (default: :obj:`False`)
'''
channel_location_dict = SEED_IV_CHANNEL_LOCATION_DICT
adjacency_matrix = SEED_IV_ADJACENCY_MATRIX
def __init__(self,
root_path: str = './eeg_feature_smooth',
feature: list = ['de_movingAve'],
num_channel: int = 62,
online_transform: Union[None, Callable] = None,
offline_transform: Union[None, Callable] = None,
label_transform: Union[None, Callable] = None,
before_trial: Union[None, Callable] = None,
after_trial: Union[Callable, None] = None,
after_session: Union[Callable, None] = None,
after_subject: Union[Callable, None] = None,
io_path: str = './io/seed_iv_feature',
io_size: int = 10485760,
io_mode: str = 'lmdb',
num_worker: int = 0,
verbose: bool = True,
in_memory: bool = False):
# pass all arguments to super class
params = {
'root_path': root_path,
'feature': feature,
'num_channel': num_channel,
'online_transform': online_transform,
'offline_transform': offline_transform,
'label_transform': label_transform,
'before_trial': before_trial,
'after_trial': after_trial,
'after_session': after_session,
'after_subject': after_subject,
'io_path': io_path,
'io_size': io_size,
'io_mode': io_mode,
'num_worker': num_worker,
'verbose': verbose,
'in_memory': in_memory
}
super().__init__(**params)
# save all arguments to __dict__
self.__dict__.update(params)
@staticmethod
def process_record(feature: list = ['de_movingAve'],
num_channel: int = 62,
offline_transform: Union[None, Callable] = None,
before_trial: Union[None, Callable] = None,
after_trial: Union[Callable, None] = None,
file: Any = None,
**kwargs):
file_path = file # an element from file name list
session_id = os.path.basename(os.path.dirname(file_path))
_, file_name = os.path.split(file_path)
subject = int(os.path.basename(file_name).split('.')[0].split('_')
[0]) # subject (15)
date = int(os.path.basename(file_name).split('.')[0].split('_')
[1]) # period (3)
samples = scio.loadmat(file_path,
verify_compressed_data_integrity=False
) # trial (15), channel(62), timestep(n*200)
# label file
labels = [
[
1, 2, 3, 0, 2, 0, 0, 1, 0, 1, 2, 1, 1, 1, 2, 3, 2, 2, 3, 3, 0,
3, 0, 3
],
[
2, 1, 3, 0, 0, 2, 0, 2, 3, 3, 2, 3, 2, 0, 1, 1, 2, 1, 0, 3, 0,
1, 3, 1
],
[
1, 2,
2, 1,
3, 3,
3, 1,
1, 2,
1, 0,
2, 3,
3, 0,
2, 3,
0, 0,
2, 0,
1, 0
]
] # The labels with 0, 1, 2, and 3 denote the ground truth, neutral, sad, fear, and happy emotions, respectively.
session_labels = labels[int(session_id) - 1]
trial_ids = [
int(re.findall(r"de_movingAve(\d+)", key)[0])
for key in samples.keys() if 'de_movingAve' in key
]
write_pointer = 0
# loop for each trial
for trial_id in trial_ids:
# extract baseline signals
trial_samples = []
for cur_feature in feature:
trial_samples.append(samples[
cur_feature +
str(trial_id)]) # channel(61), timestep(n), bands(5)
trial_samples = np.concatenate(
trial_samples,
axis=-1)[:
num_channel] # channel(61), timestep(n), features(5*k)
trial_samples = trial_samples.transpose((1, 0, 2))
# timestep(n), channel(61), features(5*k)
if before_trial:
trial_samples = before_trial(trial_samples)
# record the common meta info
trial_meta_info = {
'subject_id': subject,
'trial_id': trial_id,
'session_id': session_id,
'emotion': int(session_labels[trial_id - 1]),
'date': date
}
trial_queue = []
for i, clip_sample in enumerate(trial_samples):
t_eeg = clip_sample
if not offline_transform is None:
t_eeg = offline_transform(eeg=clip_sample)['eeg']
clip_id = f'{file_name}_{write_pointer}'
write_pointer += 1
# record meta info for each signal
record_info = {
'start_at': i * 400,
'end_at': (i + 1) *
400, # The size of the sliding time windows for feature extraction is 4 seconds.
'clip_id': clip_id
}
record_info.update(trial_meta_info)
yield {'eeg': t_eeg, 'key': clip_id, 'info': record_info}
def set_records(self, root_path: str = './eeg_feature_smooth', **kwargs):
session_list = ['1', '2', '3']
file_path_list = []
for session in session_list:
session_root_path = os.path.join(root_path, session)
for file_name in os.listdir(session_root_path):
file_path_list.append(os.path.join(session_root_path,
file_name))
return file_path_list
def __getitem__(self, index: int) -> Tuple[any, any, int, int, int]:
info = self.read_info(index)
eeg_index = str(info['clip_id'])
eeg_record = str(info['_record_id'])
eeg = self.read_eeg(eeg_record, eeg_index)
signal = eeg
label = info
if self.online_transform:
signal = self.online_transform(eeg=eeg)['eeg']
if self.label_transform:
label = self.label_transform(y=info)['y']
return signal, label
@property
def repr_body(self) -> Dict:
return dict(
super().repr_body, **{
'root_path': self.root_path,
'feature': self.feature,
'num_channel': self.num_channel,
'online_transform': self.online_transform,
'offline_transform': self.offline_transform,
'label_transform': self.label_transform,
'before_trial': self.before_trial,
'after_trial': self.after_trial,
'num_worker': self.num_worker,
'verbose': self.verbose,
'io_size': self.io_size
})
